Physics 361 Spring 2025 Reference Page
Machine Learning in Physics
Course Search & Enroll
Course Description Page
Instructors by Semester
Typically Offered | Occasional |
---|---|
Level | Intermediate |
Students | Undergraduate, advanced |
Credits | 3.00 |
Breadth | Physical Science |
L&S Credit | Counts for L&S degree |
A detailed introduction to the use of machine learning techniques in physics. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. A selection of physics topics that are particularly amenable to analysis using machine learning will be discussed. These might include processing collider data, classifying astronomical images, solving the Ising model, parameter estimation from physics data sets, learning physical probability distributions, finding string theory compactifications, and finding symbolic physical laws.
Prerequisites: MATH 234 and (PHYSICS 104, 202, 208, or 248), or graduate/professional standing
Lecture
Sec | Instructor | Time | Place |
---|---|---|---|
001 | Moritz Munchmeyer | TR 02:30 pm - 03:45 pm | 2120 Chamberlin |
This is an accordion element with a series of buttons that open and close related content panels.